/honey-gain
Show Honey's benchmark scoreboard — the committed quality and token results per task tier (code, user-facing, agent-to-agent) from bench/. Reports only the reproducible committed figures, never invents per-repo numbers. Use when asked how much Honey saves, how it compares to
Claude Code
$ npx -y skills add Green-PT/honey-for-devs --skill honey-gain --agent claude-code
Codex
$ npx skills add Green-PT/honey-for-devs --skill honey-gain -a codex -y
Cursor
$ npx skills add Green-PT/honey-for-devs --skill honey-gain -a cursor -y
Gemini CLI
$ npx skills add Green-PT/honey-for-devs --skill honey-gain -a gemini-cli -y
GitHub Copilot
$ npx skills add Green-PT/honey-for-devs --skill honey-gain -a github-copilot -y
OpenCode
$ npx skills add Green-PT/honey-for-devs --skill honey-gain -a opencode -y
Windsurf
$ npx skills add Green-PT/honey-for-devs --skill honey-gain -a windsurf -y
SKILL.md
honey-gain.SKILL.mdname: honey-gain
description: >-
Show Honey's benchmark scoreboard — the committed quality and token results per
task tier (code, user-facing, agent-to-agent) from bench/. Reports only the
reproducible committed figures, never invents per-repo numbers. Use when asked
how much Honey saves, how it compares to Caveman / Ponytail / no-skill baseline,
or for the headline numbers.
license: MIT
Honey Gain
Report the **committed** benchmark results — never a guessed or per-session number, and never an embedded copy that can drift from the bench.
Do
1. Recompute from the committed records at use time — don't recite from memory, and prefer the raw records over any rendered table (renderings go stale, the records don't):
cd bench && node src/report.js --stamp full-opus48 --by-type
Offline, no API spend. Swap `--stamp full-gpt55` for the cross-provider figure, drop `--by-type` for the whole suite. Hive handoff numbers → [`bench/hive/RESULTS.md`](../../bench/hive/RESULTS.md). 2. Report the tier table terse: **Δ LOC and Δ output, each with its `p`**, judge as win/loss/tie, and the test pass-rate, per variant. The tier split *is* the finding — deepest on code and handoffs, output a statistical tie on user-facing (the polish carve-out). Lead with **Δ LOC**: it measures Lever 1 directly, while output tokens mix code with the prose around it, and the two come apart (Ponytail cuts lines but narrates at length).
Rules
- **Never quote a delta without its p-value**, and call `(ns)` results ties, not wins.
Every figure is a paired per-task median; a ratio of arm totals is not quotable.
- Prose renderings ([`bench/README.md`](../../bench/README.md#results), `results/combined.md`)
are secondary. If one disagrees with a fresh `--stamp` recompute, the recompute wins — say the rendering is out of sync.
- Quality is a **tie**, not a gain — that's the honest claim. Don't upgrade it.
- Cost/CO₂ savings are a *modelled counterfactual*, not measured; those belong to
`honey-eco`, which labels them. Don't state a dollar saving here.
- Asked for numbers on **this** repo? The bench measures the skill on a fixed task suite,
not the user's codebase — offer `cd bench && npm run bench`, don't extrapolate.
- One honest caveat, once: 23 author-written tasks, judge noise — the objective test-pass
column is the trustworthy correctness signal.
- Never resurrect the old unreproducible `92%/78%/73%` / `−57%/−65%/−70%` numbers, or the
superseded arm-total figures (`−49%` code, `−15%` aggregate) — see [`bench/METHODOLOGY.md`](../../bench/METHODOLOGY.md).
Read more
name: honey-gain description: >- Show Honey's benchmark scoreboard — the committed quality and token results per task tier (code, user-facing, agent-to-agent) from bench/. Reports only the reproducible committed figures, never invents per-repo numbers. Use when asked how much Honey saves, how it compares to Caveman / Ponytail / no-skill baseline, or for the headline numbers. license: MIT
Honey Gain
Report the **committed** benchmark results — never a guessed or per-session number, and never an embedded copy that can drift from the bench.
Do
1. Recompute from the committed records at use time — don't recite from memory, and prefer the raw records over any rendered table (renderings go stale, the records don't):
cd bench && node src/report.js --stamp full-opus48 --by-type
Offline, no API spend. Swap `--stamp full-gpt55` for the cross-provider figure, drop `--by-type` for the whole suite. Hive handoff numbers → [`bench/hive/RESULTS.md`](../../bench/hive/RESULTS.md). 2. Report the tier table terse: **Δ LOC and Δ output, each with its `p`**, judge as win/loss/tie, and the test pass-rate, per variant. The tier split *is* the finding — deepest on code and handoffs, output a statistical tie on user-facing (the polish carve-out). Lead with **Δ LOC**: it measures Lever 1 directly, while output tokens mix code with the prose around it, and the two come apart (Ponytail cuts lines but narrates at length).
Rules
- **Never quote a delta without its p-value**, and call `(ns)` results ties, not wins.
Every figure is a paired per-task median; a ratio of arm totals is not quotable.
- Prose renderings ([`bench/README.md`](../../bench/README.md#results), `results/combined.md`)
are secondary. If one disagrees with a fresh `--stamp` recompute, the recompute wins — say the rendering is out of sync.
- Quality is a **tie**, not a gain — that's the honest claim. Don't upgrade it.
- Cost/CO₂ savings are a *modelled counterfactual*, not measured; those belong to
`honey-eco`, which labels them. Don't state a dollar saving here.
- Asked for numbers on **this** repo? The bench measures the skill on a fixed task suite,
not the user's codebase — offer `cd bench && npm run bench`, don't extrapolate.
- One honest caveat, once: 23 author-written tasks, judge noise — the objective test-pass
column is the trustworthy correctness signal.
- Never resurrect the old unreproducible `92%/78%/73%` / `−57%/−65%/−70%` numbers, or the
superseded arm-total figures (`−49%` code, `−15%` aggregate) — see [`bench/METHODOLOGY.md`](../../bench/METHODOLOGY.md).
Write less code and say less about it. Honey (I Shrunk the AI) by GreenPT is a cross-tool coding skill that cuts the output tokens AI coding agents spend — making them emit less code and less prose without losing correctness.
Other skills on honey.
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brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
ask-matt
Ask which skill or flow fits your situation. A router over the skills in this repo.
Questions about honey-gain.
- Which coding agents can use honey-gain?
- honey-gain installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

